AI Explains AI: Fiddler Develops Model Explainability for Transparency

Startup develops explainable AI for companies to understand their AI models and give consumers answers.
by Scott Martin

Your online loan application just got declined without explanation. Welcome to the AI black box.

Businesses of all stripes turn to AI for computerized decisions driven by data. Yet consumers using applications with AI get left in the dark on how automated decisions work. And many people working within companies have no idea how to explain the inner workings of AI to customers.

Fiddler Labs wants to change that.

The San Francisco-based startup offers an explainable AI platform that enables companies to explain, monitor and analyze their AI products.

Explainable AI is a growing area of interest for enterprises because those outside of engineering often need to understand how their AI models work.

Using explainable AI, banks can provide reasons to customers for a loan’s rejection, based on data points fed to models, such as maxed credit cards or high debt-to-income ratios. Internally, marketers can strategize about customers and products by knowing more about the data points that drive them.

“This is bridging the gap between hardcore data scientists who are building the models and the business teams using these models to make decisions,” said Anusha Sethuraman, head of product marketing at Fiddler Labs.

Fiddler Labs is a member of NVIDIA Inception, a program that enables companies working in AI and data science with fundamental tools, expertise and marketing support, and helps them get to market faster.

What Is Explainable AI?

Explainable AI is a set of tools and techniques that help explore the math inside an AI model. It can map out the data inputs and their weighted values that were used to arrive at the data output of the model.

All of this, essentially, enables a layperson to study the sausage factory at work on the inside of an otherwise opaque process. The result is explainable AI can help deliver insights into how and why a particular decision was made by a model.

“There’s often a hurdle to get AI into production. Explainability is one of the things that we think can address this hurdle,” Sethuraman said.

With an ensemble of models often at use, creating this is no easy job.

But Fiddler Labs CEO and co-founder Krishna Gade is up to the task. He previously led the team at Facebook that built the “Why am I seeing this post?” feature to help consumers and internal teams understand how its AI works in the Facebook news feed.

He and Amit Paka — a University of Minnesota classmate — joined forces and quit their jobs to start Fiddler Labs. Paka, the company’s chief product officer, was motivated by his experience at Samsung with shopping recommendation apps and the lack of understanding into how these AI recommendation models work.

Explainability for Transparency

Founded in 2018, Fiddler Labs offers explainability for greater transparency in businesses. It helps companies make better informed business decisions through a combination of data, explainable AI and human oversight, according to Sethuraman.

Fiddler’s tech is used by Hired, a talent and job matchmaking site driven by AI. Fiddler provides real-time reporting on how Hired’s AI models are working. It can generate explanations on candidate assessments and provide bias monitoring feedback, allowing Hired to assess its AI.

Explainable AI needs to be quickly available for consumer fintech applications. That enables customer service representatives to explain automated financial decisions — like loan rejections and robo rates — and build trust with transparency about the process.

The algorithms used for explanations require hefty processing. Sethuraman said that Fiddler Labs taps into NVIDIA cloud GPUs to make this possible, saying CPUs aren’t up to the task.

“You can’t wait 30 seconds for the explanations — you want explanations within milliseconds on a lot of different things depending on the use cases,” Sethuraman said.

Visit NVIDIA’s financial services industry page to learn more.

Image credit: Emily Morter, via the Unsplash Photo Community.